Overview

Dataset statistics

Number of variables20
Number of observations10127
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory160.0 B

Variable types

Numeric14
Categorical6

Alerts

Customer_Age is highly correlated with Months_on_bookHigh correlation
Months_on_book is highly correlated with Customer_AgeHigh correlation
Credit_Limit is highly correlated with Avg_Open_To_BuyHigh correlation
Total_Revolving_Bal is highly correlated with Avg_Utilization_RatioHigh correlation
Avg_Open_To_Buy is highly correlated with Credit_Limit and 1 other fieldsHigh correlation
Total_Trans_Amt is highly correlated with Total_Trans_CtHigh correlation
Total_Trans_Ct is highly correlated with Total_Trans_AmtHigh correlation
Avg_Utilization_Ratio is highly correlated with Total_Revolving_Bal and 1 other fieldsHigh correlation
Customer_Age is highly correlated with Months_on_bookHigh correlation
Months_on_book is highly correlated with Customer_AgeHigh correlation
Credit_Limit is highly correlated with Avg_Open_To_BuyHigh correlation
Total_Revolving_Bal is highly correlated with Avg_Utilization_RatioHigh correlation
Avg_Open_To_Buy is highly correlated with Credit_Limit and 1 other fieldsHigh correlation
Total_Trans_Amt is highly correlated with Total_Trans_CtHigh correlation
Total_Trans_Ct is highly correlated with Total_Trans_AmtHigh correlation
Avg_Utilization_Ratio is highly correlated with Total_Revolving_Bal and 1 other fieldsHigh correlation
Customer_Age is highly correlated with Months_on_bookHigh correlation
Months_on_book is highly correlated with Customer_AgeHigh correlation
Credit_Limit is highly correlated with Avg_Open_To_BuyHigh correlation
Total_Revolving_Bal is highly correlated with Avg_Utilization_RatioHigh correlation
Avg_Open_To_Buy is highly correlated with Credit_Limit and 1 other fieldsHigh correlation
Total_Trans_Amt is highly correlated with Total_Trans_CtHigh correlation
Total_Trans_Ct is highly correlated with Total_Trans_AmtHigh correlation
Avg_Utilization_Ratio is highly correlated with Total_Revolving_Bal and 1 other fieldsHigh correlation
Sex is highly correlated with Income_CategoryHigh correlation
Income_Category is highly correlated with SexHigh correlation
Customer_Age is highly correlated with Dependent_Count and 1 other fieldsHigh correlation
Sex is highly correlated with Income_Category and 2 other fieldsHigh correlation
Dependent_Count is highly correlated with Customer_AgeHigh correlation
Income_Category is highly correlated with SexHigh correlation
Card_Category is highly correlated with Credit_Limit and 1 other fieldsHigh correlation
Months_on_book is highly correlated with Customer_AgeHigh correlation
Credit_Limit is highly correlated with Sex and 3 other fieldsHigh correlation
Total_Revolving_Bal is highly correlated with Avg_Utilization_Ratio and 1 other fieldsHigh correlation
Avg_Open_To_Buy is highly correlated with Sex and 3 other fieldsHigh correlation
Total_Amt_Chng_Q4_Q1 is highly correlated with Total_Ct_Chng_Q4_Q1High correlation
Total_Trans_Amt is highly correlated with Total_Trans_CtHigh correlation
Total_Trans_Ct is highly correlated with Total_Trans_Amt and 1 other fieldsHigh correlation
Total_Ct_Chng_Q4_Q1 is highly correlated with Total_Amt_Chng_Q4_Q1High correlation
Avg_Utilization_Ratio is highly correlated with Credit_Limit and 2 other fieldsHigh correlation
Churn is highly correlated with Total_Revolving_Bal and 1 other fieldsHigh correlation
Dependent_Count has 904 (8.9%) zeros Zeros
Contacts_Count_12_mon has 399 (3.9%) zeros Zeros
Total_Revolving_Bal has 2470 (24.4%) zeros Zeros
Avg_Utilization_Ratio has 2470 (24.4%) zeros Zeros

Reproduction

Analysis started2021-12-30 04:17:50.958378
Analysis finished2021-12-30 04:20:30.871867
Duration2 minutes and 39.91 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Customer_Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.3259603
Minimum26
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2021-12-30T05:20:31.213110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33
Q141
median46
Q352
95-th percentile60
Maximum73
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.016814033
Coefficient of variation (CV)0.1730523011
Kurtosis-0.2886199153
Mean46.3259603
Median Absolute Deviation (MAD)6
Skewness-0.03360501632
Sum469143
Variance64.26930723
MonotonicityNot monotonic
2021-12-30T05:20:31.860642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
44500
 
4.9%
49495
 
4.9%
46490
 
4.8%
45486
 
4.8%
47479
 
4.7%
43473
 
4.7%
48472
 
4.7%
50452
 
4.5%
42426
 
4.2%
51398
 
3.9%
Other values (35)5456
53.9%
ValueCountFrequency (%)
2678
0.8%
2732
 
0.3%
2829
 
0.3%
2956
 
0.6%
3070
 
0.7%
3191
0.9%
32106
1.0%
33127
1.3%
34146
1.4%
35184
1.8%
ValueCountFrequency (%)
731
 
< 0.1%
701
 
< 0.1%
682
 
< 0.1%
674
 
< 0.1%
662
 
< 0.1%
65101
1.0%
6443
0.4%
6365
0.6%
6293
0.9%
6193
0.9%

Sex
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
F
5358 
M
4769 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F5358
52.9%
M4769
47.1%

Length

2021-12-30T05:20:32.775264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-30T05:20:33.085512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
f5358
52.9%
m4769
47.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Dependent_Count
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.346203219
Minimum0
Maximum5
Zeros904
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2021-12-30T05:20:33.403738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.298908349
Coefficient of variation (CV)0.5536214162
Kurtosis-0.6830166531
Mean2.346203219
Median Absolute Deviation (MAD)1
Skewness-0.02082553562
Sum23760
Variance1.687162899
MonotonicityNot monotonic
2021-12-30T05:20:33.865066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
32732
27.0%
22655
26.2%
11838
18.1%
41574
15.5%
0904
 
8.9%
5424
 
4.2%
ValueCountFrequency (%)
0904
 
8.9%
11838
18.1%
22655
26.2%
32732
27.0%
41574
15.5%
5424
 
4.2%
ValueCountFrequency (%)
5424
 
4.2%
41574
15.5%
32732
27.0%
22655
26.2%
11838
18.1%
0904
 
8.9%

Education_Level
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Graduate
3128 
High School
2013 
Unknown
1519 
Uneducated
1487 
College
1013 
Other values (2)
967 

Length

Max length13
Median length8
Mean length8.939271255
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh School
2nd rowGraduate
3rd rowGraduate
4th rowHigh School
5th rowUneducated

Common Values

ValueCountFrequency (%)
Graduate3128
30.9%
High School2013
19.9%
Unknown1519
15.0%
Uneducated1487
14.7%
College1013
 
10.0%
Post-Graduate516
 
5.1%
Doctorate451
 
4.5%

Length

2021-12-30T05:20:34.388438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-30T05:20:34.776714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
graduate3128
25.8%
high2013
16.6%
school2013
16.6%
unknown1519
12.5%
uneducated1487
12.2%
college1013
 
8.3%
post-graduate516
 
4.3%
doctorate451
 
3.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Marital_Status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Married
4687 
Single
3943 
Unknown
749 
Divorced
748 

Length

Max length8
Median length7
Mean length6.684506764
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowSingle
3rd rowMarried
4th rowUnknown
5th rowMarried

Common Values

ValueCountFrequency (%)
Married4687
46.3%
Single3943
38.9%
Unknown749
 
7.4%
Divorced748
 
7.4%

Length

2021-12-30T05:20:35.291051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-30T05:20:35.673323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
married4687
46.3%
single3943
38.9%
unknown749
 
7.4%
divorced748
 
7.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Income_Category
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Less than $40K
3561 
$40K - $60K
1790 
$80K - $120K
1535 
$60K - $80K
1402 
Unknown
1112 

Length

Max length14
Median length12
Mean length11.4801027
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$60K - $80K
2nd rowLess than $40K
3rd row$80K - $120K
4th rowLess than $40K
5th row$60K - $80K

Common Values

ValueCountFrequency (%)
Less than $40K3561
35.2%
$40K - $60K1790
17.7%
$80K - $120K1535
15.2%
$60K - $80K1402
 
13.8%
Unknown1112
 
11.0%
$120K +727
 
7.2%

Length

2021-12-30T05:20:36.085616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-30T05:20:36.458882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
5454
19.9%
40k5351
19.5%
less3561
13.0%
than3561
13.0%
60k3192
11.6%
80k2937
10.7%
120k2262
8.2%
unknown1112
 
4.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Card_Category
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Blue
9436 
Silver
 
555
Gold
 
116
Platinum
 
20

Length

Max length8
Median length4
Mean length4.117507653
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowBlue
3rd rowBlue
4th rowBlue
5th rowBlue

Common Values

ValueCountFrequency (%)
Blue9436
93.2%
Silver555
 
5.5%
Gold116
 
1.1%
Platinum20
 
0.2%

Length

2021-12-30T05:20:38.170104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-30T05:20:38.546372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
blue9436
93.2%
silver555
 
5.5%
gold116
 
1.1%
platinum20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Months_on_book
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.9284092
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2021-12-30T05:20:39.000688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q131
median36
Q340
95-th percentile50
Maximum56
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.986416331
Coefficient of variation (CV)0.2222869453
Kurtosis0.4001001202
Mean35.9284092
Median Absolute Deviation (MAD)4
Skewness-0.1065653599
Sum363847
Variance63.78284581
MonotonicityNot monotonic
2021-12-30T05:20:39.609119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
362463
24.3%
37358
 
3.5%
34353
 
3.5%
38347
 
3.4%
39341
 
3.4%
40333
 
3.3%
31318
 
3.1%
35317
 
3.1%
33305
 
3.0%
30300
 
3.0%
Other values (34)4692
46.3%
ValueCountFrequency (%)
1370
0.7%
1416
 
0.2%
1534
 
0.3%
1629
 
0.3%
1739
 
0.4%
1858
0.6%
1963
0.6%
2074
0.7%
2183
0.8%
22105
1.0%
ValueCountFrequency (%)
56103
1.0%
5542
 
0.4%
5453
 
0.5%
5378
0.8%
5262
 
0.6%
5180
0.8%
5096
0.9%
49141
1.4%
48162
1.6%
47171
1.7%

Total_Relationship_Count
Real number (ℝ≥0)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.812580231
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2021-12-30T05:20:40.109482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.554407865
Coefficient of variation (CV)0.4077049586
Kurtosis-1.006130507
Mean3.812580231
Median Absolute Deviation (MAD)1
Skewness-0.162452415
Sum38610
Variance2.416183812
MonotonicityNot monotonic
2021-12-30T05:20:40.559823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
32305
22.8%
41912
18.9%
51891
18.7%
61866
18.4%
21243
12.3%
1910
 
9.0%
ValueCountFrequency (%)
1910
 
9.0%
21243
12.3%
32305
22.8%
41912
18.9%
51891
18.7%
61866
18.4%
ValueCountFrequency (%)
61866
18.4%
51891
18.7%
41912
18.9%
32305
22.8%
21243
12.3%
1910
 
9.0%

Months_Inactive_12_mon
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.341167177
Minimum0
Maximum6
Zeros29
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2021-12-30T05:20:40.993109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.010622399
Coefficient of variation (CV)0.4316745978
Kurtosis1.098522614
Mean2.341167177
Median Absolute Deviation (MAD)1
Skewness0.633061129
Sum23709
Variance1.021357634
MonotonicityNot monotonic
2021-12-30T05:20:41.459462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33846
38.0%
23282
32.4%
12233
22.0%
4435
 
4.3%
5178
 
1.8%
6124
 
1.2%
029
 
0.3%
ValueCountFrequency (%)
029
 
0.3%
12233
22.0%
23282
32.4%
33846
38.0%
4435
 
4.3%
5178
 
1.8%
6124
 
1.2%
ValueCountFrequency (%)
6124
 
1.2%
5178
 
1.8%
4435
 
4.3%
33846
38.0%
23282
32.4%
12233
22.0%
029
 
0.3%

Contacts_Count_12_mon
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.455317468
Minimum0
Maximum6
Zeros399
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2021-12-30T05:20:41.923792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.106225143
Coefficient of variation (CV)0.4505426109
Kurtosis0.0008626566254
Mean2.455317468
Median Absolute Deviation (MAD)1
Skewness0.01100562622
Sum24865
Variance1.223734066
MonotonicityNot monotonic
2021-12-30T05:20:42.363105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33380
33.4%
23227
31.9%
11499
14.8%
41392
13.7%
0399
 
3.9%
5176
 
1.7%
654
 
0.5%
ValueCountFrequency (%)
0399
 
3.9%
11499
14.8%
23227
31.9%
33380
33.4%
41392
13.7%
5176
 
1.7%
654
 
0.5%
ValueCountFrequency (%)
654
 
0.5%
5176
 
1.7%
41392
13.7%
33380
33.4%
23227
31.9%
11499
14.8%
0399
 
3.9%

Credit_Limit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6205
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8631.953698
Minimum1438.3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2021-12-30T05:20:42.972537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1438.3
5-th percentile1438.51
Q12555
median4549
Q311067.5
95-th percentile34516
Maximum34516
Range33077.7
Interquartile range (IQR)8512.5

Descriptive statistics

Standard deviation9088.77665
Coefficient of variation (CV)1.052922313
Kurtosis1.808989336
Mean8631.953698
Median Absolute Deviation (MAD)2593
Skewness1.666725808
Sum87415795.1
Variance82605861
MonotonicityNot monotonic
2021-12-30T05:20:43.787117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34516508
 
5.0%
1438.3507
 
5.0%
995918
 
0.2%
1598718
 
0.2%
2398112
 
0.1%
249011
 
0.1%
622411
 
0.1%
373511
 
0.1%
746910
 
0.1%
20698
 
0.1%
Other values (6195)9013
89.0%
ValueCountFrequency (%)
1438.3507
5.0%
14392
 
< 0.1%
14401
 
< 0.1%
14412
 
< 0.1%
14421
 
< 0.1%
14433
 
< 0.1%
14461
 
< 0.1%
14492
 
< 0.1%
14512
 
< 0.1%
14522
 
< 0.1%
ValueCountFrequency (%)
34516508
5.0%
344961
 
< 0.1%
344581
 
< 0.1%
344271
 
< 0.1%
341981
 
< 0.1%
341731
 
< 0.1%
341621
 
< 0.1%
341401
 
< 0.1%
340581
 
< 0.1%
340101
 
< 0.1%

Total_Revolving_Bal
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1974
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.814061
Minimum0
Maximum2517
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2021-12-30T05:20:44.848849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1359
median1276
Q31784
95-th percentile2517
Maximum2517
Range2517
Interquartile range (IQR)1425

Descriptive statistics

Standard deviation814.9873352
Coefficient of variation (CV)0.7008750257
Kurtosis-1.145991782
Mean1162.814061
Median Absolute Deviation (MAD)591
Skewness-0.1488372503
Sum11775818
Variance664204.3566
MonotonicityNot monotonic
2021-12-30T05:20:45.550342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02470
 
24.4%
2517508
 
5.0%
196512
 
0.1%
148012
 
0.1%
143411
 
0.1%
166411
 
0.1%
172011
 
0.1%
159010
 
0.1%
154210
 
0.1%
152810
 
0.1%
Other values (1964)7062
69.7%
ValueCountFrequency (%)
02470
24.4%
1321
 
< 0.1%
1341
 
< 0.1%
1451
 
< 0.1%
1541
 
< 0.1%
1571
 
< 0.1%
1592
 
< 0.1%
1682
 
< 0.1%
1701
 
< 0.1%
1861
 
< 0.1%
ValueCountFrequency (%)
2517508
5.0%
25143
 
< 0.1%
25131
 
< 0.1%
25122
 
< 0.1%
25111
 
< 0.1%
25092
 
< 0.1%
25082
 
< 0.1%
25074
 
< 0.1%
25061
 
< 0.1%
25053
 
< 0.1%

Avg_Open_To_Buy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6813
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7469.139637
Minimum3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2021-12-30T05:20:46.202805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile480.3
Q11324.5
median3474
Q39859
95-th percentile32183.4
Maximum34516
Range34513
Interquartile range (IQR)8534.5

Descriptive statistics

Standard deviation9090.685324
Coefficient of variation (CV)1.217099394
Kurtosis1.798617296
Mean7469.139637
Median Absolute Deviation (MAD)2665
Skewness1.661696546
Sum75639977.1
Variance82640559.65
MonotonicityNot monotonic
2021-12-30T05:20:46.901330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3324
 
3.2%
3451698
 
1.0%
3199926
 
0.3%
7878
 
0.1%
7017
 
0.1%
7137
 
0.1%
9537
 
0.1%
4637
 
0.1%
9906
 
0.1%
7886
 
0.1%
Other values (6803)9631
95.1%
ValueCountFrequency (%)
31
< 0.1%
101
< 0.1%
142
< 0.1%
151
< 0.1%
241
< 0.1%
281
< 0.1%
291
< 0.1%
361
< 0.1%
392
< 0.1%
412
< 0.1%
ValueCountFrequency (%)
3451698
1.0%
343621
 
< 0.1%
343021
 
< 0.1%
343001
 
< 0.1%
342971
 
< 0.1%
342861
 
< 0.1%
342381
 
< 0.1%
342271
 
< 0.1%
341401
 
< 0.1%
341191
 
< 0.1%

Total_Amt_Chng_Q4_Q1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1158
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7599406537
Minimum0
Maximum3.397
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2021-12-30T05:20:47.633822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.463
Q10.631
median0.736
Q30.859
95-th percentile1.103
Maximum3.397
Range3.397
Interquartile range (IQR)0.228

Descriptive statistics

Standard deviation0.2192067692
Coefficient of variation (CV)0.288452484
Kurtosis9.993501179
Mean0.7599406537
Median Absolute Deviation (MAD)0.114
Skewness1.732063411
Sum7695.919
Variance0.04805160768
MonotonicityNot monotonic
2021-12-30T05:20:48.367372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.79136
 
0.4%
0.71234
 
0.3%
0.74334
 
0.3%
0.71833
 
0.3%
0.73533
 
0.3%
0.74432
 
0.3%
0.69932
 
0.3%
0.72232
 
0.3%
0.73131
 
0.3%
0.63131
 
0.3%
Other values (1148)9799
96.8%
ValueCountFrequency (%)
05
< 0.1%
0.011
 
< 0.1%
0.0181
 
< 0.1%
0.0461
 
< 0.1%
0.0612
 
< 0.1%
0.0721
 
< 0.1%
0.1011
 
< 0.1%
0.121
 
< 0.1%
0.1531
 
< 0.1%
0.1631
 
< 0.1%
ValueCountFrequency (%)
3.3971
< 0.1%
3.3551
< 0.1%
2.6751
< 0.1%
2.5941
< 0.1%
2.3681
< 0.1%
2.3571
< 0.1%
2.3161
< 0.1%
2.2821
< 0.1%
2.2751
< 0.1%
2.2711
< 0.1%

Total_Trans_Amt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5033
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4404.086304
Minimum510
Maximum18484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2021-12-30T05:20:49.086854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile1283.3
Q12155.5
median3899
Q34741
95-th percentile14212
Maximum18484
Range17974
Interquartile range (IQR)2585.5

Descriptive statistics

Standard deviation3397.129254
Coefficient of variation (CV)0.7713584656
Kurtosis3.894023406
Mean4404.086304
Median Absolute Deviation (MAD)1308
Skewness2.041003403
Sum44600182
Variance11540487.17
MonotonicityNot monotonic
2021-12-30T05:20:49.783349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
425311
 
0.1%
450911
 
0.1%
451810
 
0.1%
222910
 
0.1%
42209
 
0.1%
48699
 
0.1%
40379
 
0.1%
43139
 
0.1%
44989
 
0.1%
40429
 
0.1%
Other values (5023)10031
99.1%
ValueCountFrequency (%)
5101
< 0.1%
5301
< 0.1%
5631
< 0.1%
5691
< 0.1%
5941
< 0.1%
5961
< 0.1%
5971
< 0.1%
6021
< 0.1%
6151
< 0.1%
6431
< 0.1%
ValueCountFrequency (%)
184841
< 0.1%
179951
< 0.1%
177441
< 0.1%
176341
< 0.1%
176281
< 0.1%
174981
< 0.1%
174371
< 0.1%
173901
< 0.1%
173501
< 0.1%
172581
< 0.1%

Total_Trans_Ct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct126
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.85869458
Minimum10
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2021-12-30T05:20:50.481874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q145
median67
Q381
95-th percentile105
Maximum139
Range129
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.47257045
Coefficient of variation (CV)0.3619032206
Kurtosis-0.3671632411
Mean64.85869458
Median Absolute Deviation (MAD)17
Skewness0.1536730685
Sum656824
Variance550.9615635
MonotonicityNot monotonic
2021-12-30T05:20:51.189376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81208
 
2.1%
71203
 
2.0%
75203
 
2.0%
69202
 
2.0%
82202
 
2.0%
76198
 
2.0%
77197
 
1.9%
70193
 
1.9%
74190
 
1.9%
78190
 
1.9%
Other values (116)8141
80.4%
ValueCountFrequency (%)
104
 
< 0.1%
112
 
< 0.1%
124
 
< 0.1%
135
 
< 0.1%
149
 
0.1%
1516
0.2%
1613
0.1%
1713
0.1%
1823
0.2%
1911
0.1%
ValueCountFrequency (%)
1391
 
< 0.1%
1381
 
< 0.1%
1341
 
< 0.1%
1321
 
< 0.1%
1316
0.1%
1305
< 0.1%
1296
0.1%
12810
0.1%
12712
0.1%
12610
0.1%

Total_Ct_Chng_Q4_Q1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct830
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7122223758
Minimum0
Maximum3.714
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2021-12-30T05:20:51.893877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.368
Q10.582
median0.702
Q30.818
95-th percentile1.069
Maximum3.714
Range3.714
Interquartile range (IQR)0.236

Descriptive statistics

Standard deviation0.2380860913
Coefficient of variation (CV)0.3342861716
Kurtosis15.6892929
Mean0.7122223758
Median Absolute Deviation (MAD)0.119
Skewness2.064030568
Sum7212.676
Variance0.05668498689
MonotonicityNot monotonic
2021-12-30T05:20:52.560329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.667171
 
1.7%
1166
 
1.6%
0.5161
 
1.6%
0.75156
 
1.5%
0.6113
 
1.1%
0.8101
 
1.0%
0.71492
 
0.9%
0.83385
 
0.8%
0.77869
 
0.7%
0.62563
 
0.6%
Other values (820)8950
88.4%
ValueCountFrequency (%)
07
0.1%
0.0281
 
< 0.1%
0.0291
 
< 0.1%
0.0381
 
< 0.1%
0.0531
 
< 0.1%
0.0592
 
< 0.1%
0.0621
 
< 0.1%
0.0741
 
< 0.1%
0.0773
< 0.1%
0.0913
< 0.1%
ValueCountFrequency (%)
3.7141
 
< 0.1%
3.5711
 
< 0.1%
3.51
 
< 0.1%
3.251
 
< 0.1%
32
< 0.1%
2.8751
 
< 0.1%
2.751
 
< 0.1%
2.5711
 
< 0.1%
2.53
< 0.1%
2.4291
 
< 0.1%

Avg_Utilization_Ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct964
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2748935519
Minimum0
Maximum0.999
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2021-12-30T05:20:53.290869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.023
median0.176
Q30.503
95-th percentile0.793
Maximum0.999
Range0.999
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.2756914693
Coefficient of variation (CV)1.002902641
Kurtosis-0.7949719515
Mean0.2748935519
Median Absolute Deviation (MAD)0.176
Skewness0.7180079968
Sum2783.847
Variance0.07600578622
MonotonicityNot monotonic
2021-12-30T05:20:53.989366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02470
 
24.4%
0.07344
 
0.4%
0.05733
 
0.3%
0.04832
 
0.3%
0.0630
 
0.3%
0.06129
 
0.3%
0.04529
 
0.3%
0.05928
 
0.3%
0.06928
 
0.3%
0.05327
 
0.3%
Other values (954)7377
72.8%
ValueCountFrequency (%)
02470
24.4%
0.0041
 
< 0.1%
0.0051
 
< 0.1%
0.0063
 
< 0.1%
0.0071
 
< 0.1%
0.0082
 
< 0.1%
0.0091
 
< 0.1%
0.011
 
< 0.1%
0.0111
 
< 0.1%
0.0124
 
< 0.1%
ValueCountFrequency (%)
0.9991
 
< 0.1%
0.9951
 
< 0.1%
0.9941
 
< 0.1%
0.9921
 
< 0.1%
0.991
 
< 0.1%
0.9881
 
< 0.1%
0.9871
 
< 0.1%
0.9851
 
< 0.1%
0.9841
 
< 0.1%
0.9834
< 0.1%

Churn
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
0
8500 
1
1627 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08500
83.9%
11627
 
16.1%

Length

2021-12-30T05:20:54.585790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-30T05:20:54.877975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
08500
83.9%
11627
 
16.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-12-30T05:20:19.440761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:09.025931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:38.188390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:46.662383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:54.423939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:02.839947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:11.076780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:19.631919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:28.092903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:36.551306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:44.943248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:53.641429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:02.488737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:10.683559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:19.985147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:09.274109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:38.776785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:47.182760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:54.973330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:03.387336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:11.641181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:20.197321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:28.642322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:37.147730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:45.528664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:54.204850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:03.052136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:11.254966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:20.548569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:09.792506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:39.337178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:47.717133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:55.530756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:03.957714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:12.196568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:20.795746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:29.203720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:37.734147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:46.113072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:54.789244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:03.633522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:11.840381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:21.079918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:10.307851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:39.861557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:48.215490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:56.059102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:04.494095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:12.715965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:21.357117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:29.731095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:38.295545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:47.036757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:55.335626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:04.161926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:12.399779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:21.650352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:10.889285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:40.423978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:48.777915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:56.634538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:05.080540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:13.288344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-12-30T05:19:30.294475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:38.889947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:47.623173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-12-30T05:18:49.892727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:57.770324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:06.296376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:14.461178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:23.179439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:31.823926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:40.068784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:48.830032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:57.152946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:05.904163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:14.192052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:23.430589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:34.138484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:42.175194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:50.464126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:58.363739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:06.901806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-12-30T05:19:32.421371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:40.692220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:49.409414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:57.772364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-12-30T05:19:17.254230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:25.632182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:34.196633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-12-30T05:19:51.197686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:19:59.616696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-12-30T05:20:16.630785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:25.826319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:18:36.469168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-12-30T05:20:01.505016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:10.109131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-30T05:20:18.852343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-12-30T05:20:55.487422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-30T05:20:56.485139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-30T05:20:57.549875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-30T05:20:58.601615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-30T05:20:59.402212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-30T05:20:28.218045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-30T05:20:30.094369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Customer_AgeSexDependent_CountEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioChurn
045M3High SchoolMarried$60K - $80KBlue3951312691.077711914.01.3351144421.6250.0610
149F5GraduateSingleLess than $40KBlue446128256.08647392.01.5411291333.7140.1050
251M3GraduateMarried$80K - $120KBlue364103418.003418.02.5941887202.3330.0000
340F4High SchoolUnknownLess than $40KBlue343413313.02517796.01.4051171202.3330.7600
440M3UneducatedMarried$60K - $80KBlue215104716.004716.02.175816282.5000.0000
544M2GraduateMarried$40K - $60KBlue363124010.012472763.01.3761088240.8460.3110
651M4UnknownMarried$120K +Gold4661334516.0226432252.01.9751330310.7220.0660
732M0High SchoolUnknown$60K - $80KSilver2722229081.0139627685.02.2041538360.7140.0480
837M3UneducatedSingle$60K - $80KBlue3652022352.0251719835.03.3551350241.1820.1130
948M2GraduateSingle$80K - $120KBlue3663311656.016779979.01.5241441320.8820.1440

Last rows

Customer_AgeSexDependent_CountEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioChurn
1011757M2GraduateMarried$80K - $120KBlue4063417925.0190916016.00.712174981110.8200.1060
1011850M1UnknownUnknown$80K - $120KBlue366349959.09529007.00.82510310631.1000.0961
1011955F3UneducatedSingleUnknownBlue4743314657.0251712140.00.1666009530.5140.1721
1012054M1High SchoolSingle$60K - $80KBlue3452013940.0210911831.00.660155771140.7540.1510
1012156F1GraduateSingleLess than $40KBlue504143688.06063082.00.570145961200.7910.1640
1012250M2GraduateSingle$40K - $60KBlue403234003.018512152.00.703154761170.8570.4620
1012341M2UnknownDivorced$40K - $60KBlue254234277.021862091.00.8048764690.6830.5111
1012444F1High SchoolMarriedLess than $40KBlue365345409.005409.00.81910291600.8180.0001
1012530M2GraduateUnknown$40K - $60KBlue364335281.005281.00.5358395620.7220.0001
1012643F2GraduateMarriedLess than $40KSilver2562410388.019618427.00.70310294610.6490.1891